Total knee arthroplasty has become the standard of care for end-stage arthritis. The US Census Bureau data predicts that demand for primary total knee arthroplasties will grow to 3.5 million procedures annually by 2030. This will double the demand for revision knee surgery by 2015 and will increase demand 600% by 2030. Knee forces directly affect arthroplasty component survivorship, wear of articular bearing surfaces, and integrity of the bone-implant interface. Excessive knee forces accelerate breakdown of the cement interface or induces damage and collapse of the underlying bone. Knee forces and component design features also determine the contact stresses on the bearing surfaces, which are directly associated with the magnitude and distribution of material wear and damage. Knowledge of in vivo knee contact forces and stresses during all activities will be extremely valuable in clearly identifying risks for implant failure. Our design objective is to develop a system for continuously monitoring knee forces and kinematics. We will use a novel algorithm to determine knee kinematics from knee forces measured in implanted force-sensing tibial prosthesis. We will validate the results against fluoroscopically measured kinematics. We will develop a wearable data acquisition system for continuous unsupervised data monitoring. We will develop a pattern recognition algorithm to classify activities in vivo. This is a unique method of obtaining in vivo knee contact forces and kinematics together with a complete contact analysis for knee arthroplasty. The ability to monitor, characterize, and classify activities in vivo over extended periods at the proposed level of technical sophistication is novel. Data generated using this system will identify weaknesses and potential areas of failure in current designs, provide insight into enhancing the function and durability of total knee arthroplasty, and support evidence-based patient education on safe postoperative rehabilitation, recreation, and exercise. PUBLIC HEALTH RELEVANCE: The data collected will be of enormous benefit to the field of knee biomechanics in general and knee arthroplasty in particular. We will be able to continuously monitor data over extended periods of time (days or weeks) and to record naturally occurring events (in contrast to choreographed activity). Since we compute tibiofemoral contact as part of the algorithm to determine the kinematics, the forces and kinematics are already accompanied with contact analysis. We have received requests from several laboratories (including Stanford University, Harvard University, Hospital for Special Surgery, Oxford University, UK, University of Florida, Seoul National University, University of Melbourne, Australia and the Mayo Clinic) for data to develop or validate in silico and in vitro models of knee kinetics and kinematics, as well as to develop more clinically relevant wear and fatigue testing protocols. These data can be used as input into damage and wear models to predict failure or for validation of biomechanical models of the knee, which predict knee forces and kinematics. Knee designs are constantly evolving. One example includes designs that will permit greater knee function and that will allow patients to engage in activities that involve kneeling, squatting, and sitting cross-legged. Studies analyzing these activities have estimated high knee forces without in vivo validation of these forces. A higher incidence of revision knee arthroplasties is reported in patients that routinely squat, kneel, or sit cross-legged. New and existing prosthetic designs will have to be modified to withstand the anticipated increase in loading. Alternative bearings surfaces are being introduced that require more clinically relevant testing than the currently proposed standards. Continuously monitoring in vivo knee forces and kinematics under daily conditions will identify weaknesses and potential areas of failure in current and future designs and will provide insight into enhancing the function and durability of total knee arthroplasty.